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Epistasis is the phenomenon whereby one polymorphism’s effect on a trait depends on other polymorphisms present in the genome. The extent to which epistasis influences complex traits and contributes to their variation is a fundamental question in evolution and human genetics. Although often demonstrated in artificial gene manipulation studies in model organisms, and some examples have been reported in other species, few examples exist for epistasis among natural polymorphisms in human traits. Its absence from empirical findings may simply be due to low incidence in the genetic control of complex traits, but an alternative view is that it has previously been too technically challenging to detect owing to statistical and computational issues. Here we show, using advanced computation and a gene expression study design, that many instances of epistasis are found between common single nucleotide polymorphisms (SNPs). In a cohort of 846 individuals with 7,339 gene expression levels measured in peripheral blood, we found 501 significant pairwise interactions between common SNPs influencing the expression of 238 genes (P < 2.91 × 10−16). Replicaon of these interacons in two independent data sets showed both concordance of direction of epistatic effects (P = 5.56 × 10−31) and enrichment of interacon P values, with 30 being significant at a conservative threshold of P < 9.98 × 10−5. Forty‐four of the genetic interactions are
located within 5 megabases of regions of known physical chromosome interactions (P = 1.8 × 10−10). Epistac networks of three SNPs or more influence the expression levels of 129 genes, whereby one cis‐acting SNP is modulated by several trans‐acting SNPs. For example, MBNL1 is influenced by an additive effect at rs13069559, which itself is masked by trans‐SNPs on 14 different chromosomes, with nearly identical enotype–phenotype maps for each cis–trans interaction. This study presents the first evidence, to our knowledge, for many instances of segregating common polymorphisms interacting to influence human traits.
First presented at the 2014 Winter School in Mathematical and Computational Biology http://bioinformatics.org.au/ws14/program/